professor
Rachel Schutt

Jan 2014

You're either going to love this course, or hate it with a fiery passion. I loved this course. I loved it because I learned R, Python, and SQL, as well as had the opportunity to speak with professionals from leading tech companies who came in as guest lecturers. This course should not have "Introduction" in the title though. Using that word is wildly deceiving. I'm currently in a technical program but come from a not-so-technical background with limited programming experience, and this class was hard as hell. I feel confident in saying that I spent between 40 - 60 hours every single week of the semester working on something for this class. To give you a general idea, the course started with 100 students (max) on the roster with ~50 students waitlisted, we ended the semester with ~60 students on the roster. I think that says something. In regards to prerequisites, there technically are none, but this is a direct quote from the syllabus: "If you want to take this course but have holes in your background, then it’s your responsibility to fill in those holes by collaborating with others or studying and reading." And I had holes. Lots of them. This course was supposed to be split between Rachel and Kayur, but once Rachel landed a job at News Corp she rarely came to class. I believe she had to travel a lot for News Corp. For example, she Skyped in for our midterm presentations from their evil lair (London), but it was kind of messed up because it seemed as if she left Kayur out to dry who was also trying to balance his new job at Google. The lectures were designed to be almost entirely conceptual. I don't know why this was, but I generally enjoyed them because they were engaging. Occasionally, the lectures covered technical topics but they were presented in the following form: "here is a topic or formula that you need to know, go learn it on your own time". In addition to the lectures, there was a required lab which was taught by Jared Lander. The labs were where you actually learned the material you needed to know in order to complete the assignments. Jared was great, very helpful, and he often sympathized with us because of how difficult the assignments were given how little time we had to complete them. My one criticism was that he was kind of slow on uploading the lab material. Haolei Weng was the TA, and was incredibly helpful. Course Pros: You'll learn a TON from this course Course Cons: You'll have to do 90% of that learning by yourself Recommendation if you take this course: Have a solid background in R, go to all the labs and office hours, and be prepared to spend an inordinate amount of time on any given assignment. Final note: You get out of this class what you put in. You can do just about everything in R. Python is NOT taught in this course, although it should be, but it's encouraged to use. I had to learn it on my own (another contributor to the 40-60hr/wk). SQL is taught in a lab for an assignment, but there's an R package with SQL commands so you can side step learning that if you wanted. A comment on the prior post: I disagree, whole-heartedly, that Rachel and Kayur are incompetent professors. It's easy make that claim because of how the course was structured. You need to consider that this was only the second semester this course has ever been taught, and the first semester it's been taught with two professors. This is what "growing pains" looks like a company. There was a lot of disorganization, assignments were too hard for the time given, and there was unclear division of responsibilities (ie, do we contact Rachel, Kayur, Jared, or Haolei about a particular problem??). But back to Rachel and Kayur, they sincerely wanted us to learn and, personally, they always tried to help clarify something or steer me in the right direction when I engaged them directly. I think just changing the lectures from conceptual to practical would be a huge step in the right direction.

Dec 2013

This is without a doubt the worst class I have ever taken at Columbia. It was taught by two completely incompetent adjunct professors, Rachel Schutt and Kayur Patel. Rachel pretty much completely checked out pretty early in the semester once she got a better job at News Corp. She couldn't even be bothered to show up for our midterm or final presentations. I imagine Rupert Murdoch is paying her enough, so I encourage Columbia to fire her. As our main point of contact for this course with Rachel being constantly gone, Kayur failed spectacularly. He was incredibly rude to students who tried to reach out to him and consistently would email us an hour or two before a lecture with required reading. My favorite bit of classiness from Kayur came on Thanksgiving, when he emailed some of us to tell us we'd be giving our final presentations in less than a week without any prior notice. In fact, the only positive thing I have to say about Kayur is that unlike Rachel, at least he showed up. Put simply, the assignments for this course were horrible. They consisted of either mundane tasks nobody should ever have to do (sorting 100 numbers by hand was a highlight) or tasks that we had simply not been prepared to tackle. They never taught us much of anything, but certainly asked us to do quite a lot (for example, asking us to do a full implementation of Naive Bayes without ever even mentioning what Naive Bayes was in class). Some of the guest lectures were great, though I felt embarrassed on behalf of the university when they would expect the class to know very basic things and would instead be met with a sea of blank stares. Sadly, these lectures were the only part of the course that taught me anything, as Rachel and Kayur's lectures consisted only of vague platitudes and the word "data" in many different fonts on many different backgrounds. I would also like to say that as someone from a technical background my heart goes out to those who came into this class with backgrounds in nontechnical fields (Rachel and Kayur assured us there were no prerequisites). I found the work in this class to be unreasonable as I was simply not prepared for it by lecture. I cannot imagine what it must have been like for someone who had never coded before to have to take this. tl;dr: If you are student, do not take this class. If you are an official at Columbia with any power over what happens in the Statistics department, do everything you can to make sure this never happens again.

Aug 2007

Rachel is a grad student in Statistics and a competent instructor. She went a little fast sometimes but covered all the material and gave useful examples. She evidently puts a lot into her teaching and cares about how her students do. A solid section to take if you have a choice.

Apr 2007

your best bet for stat. she's dedicated and actually teaches, which is rare in the stat dept. she grades fairly and theres a decent amount of work, but any section will have that. her problems at the beginning of each class help clarify what happened last class, and her homework is a bit lengthy, but doable. the stat requirement sucks for econ majors, but this is a way to make it more manageable.